from __future__ import annotations

import uuid
from collections.abc import Callable
from enum import Enum
from itertools import islice
from operator import itemgetter
from typing import (
    TYPE_CHECKING,
    Any,
)

import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from qdrant_client import QdrantClient, models

from langchain_qdrant._utils import maximal_marginal_relevance

if TYPE_CHECKING:
    from collections.abc import Generator, Iterable, Sequence

    from langchain_qdrant.sparse_embeddings import SparseEmbeddings


class QdrantVectorStoreError(Exception):
    """`QdrantVectorStore` related exceptions."""


class RetrievalMode(str, Enum):
    """Modes for retrieving vectors from Qdrant."""

    DENSE = "dense"
    SPARSE = "sparse"
    HYBRID = "hybrid"


class QdrantVectorStore(VectorStore):
    """Qdrant vector store integration.

    Setup:
        Install `langchain-qdrant` package.

        ```bash
        pip install -qU langchain-qdrant
        ```

    Key init args — indexing params:
        collection_name: str
            Name of the collection.
        embedding: Embeddings
            Embedding function to use.
        sparse_embedding: SparseEmbeddings
            Optional sparse embedding function to use.

    Key init args — client params:
        client: QdrantClient
            Qdrant client to use.
        retrieval_mode: RetrievalMode
            Retrieval mode to use.

    Instantiate:
        ```python
        from langchain_qdrant import QdrantVectorStore
        from qdrant_client import QdrantClient
        from qdrant_client.http.models import Distance, VectorParams
        from langchain_openai import OpenAIEmbeddings

        client = QdrantClient(":memory:")

        client.create_collection(
            collection_name="demo_collection",
            vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
        )

        vector_store = QdrantVectorStore(
            client=client,
            collection_name="demo_collection",
            embedding=OpenAIEmbeddings(),
        )
        ```

    Add Documents:
        ```python
        from langchain_core.documents import Document
        from uuid import uuid4

        document_1 = Document(page_content="foo", metadata={"baz": "bar"})
        document_2 = Document(page_content="thud", metadata={"bar": "baz"})
        document_3 = Document(page_content="i will be deleted :(")

        documents = [document_1, document_2, document_3]
        ids = [str(uuid4()) for _ in range(len(documents))]
        vector_store.add_documents(documents=documents, ids=ids)
        ```

    Delete Documents:
        ```python
        vector_store.delete(ids=[ids[-1]])
        ```

    Search:
        ```python
        results = vector_store.similarity_search(
            query="thud",
            k=1,
        )
        for doc in results:
            print(f"* {doc.page_content} [{doc.metadata}]")
        ```

        ```python
        *thud[
            {
                "bar": "baz",
                "_id": "0d706099-6dd9-412a-9df6-a71043e020de",
                "_collection_name": "demo_collection",
            }
        ]
        ```

    Search with filter:
        ```python
        from qdrant_client.http import models

        results = vector_store.similarity_search(
            query="thud",
            k=1,
            filter=models.Filter(
                must=[
                    models.FieldCondition(
                        key="metadata.bar",
                        match=models.MatchValue(value="baz"),
                    )
                ]
            ),
        )
        for doc in results:
            print(f"* {doc.page_content} [{doc.metadata}]")
        ```

        ```python
        *thud[
            {
                "bar": "baz",
                "_id": "0d706099-6dd9-412a-9df6-a71043e020de",
                "_collection_name": "demo_collection",
            }
        ]
        ```

    Search with score:
        ```python
        results = vector_store.similarity_search_with_score(query="qux", k=1)
        for doc, score in results:
            print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
        ```

        ```python
        * [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}]
        ```

    Async:
        ```python
        # add documents
        # await vector_store.aadd_documents(documents=documents, ids=ids)

        # delete documents
        # await vector_store.adelete(ids=["3"])

        # search
        # results = vector_store.asimilarity_search(query="thud",k=1)

        # search with score
        results = await vector_store.asimilarity_search_with_score(query="qux", k=1)
        for doc, score in results:
            print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
        ```

        ```python
        * [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}]
        ```

    Use as Retriever:
        ```python
        retriever = vector_store.as_retriever(
            search_type="mmr",
            search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
        )
        retriever.invoke("thud")
        ```

        ```python
        [
            Document(
                metadata={
                    "bar": "baz",
                    "_id": "0d706099-6dd9-412a-9df6-a71043e020de",
                    "_collection_name": "demo_collection",
                },
                page_content="thud",
            )
        ]
        ```
    """  # noqa: E501

    CONTENT_KEY: str = "page_content"
    METADATA_KEY: str = "metadata"
    VECTOR_NAME: str = ""  # The default/unnamed vector - https://qdrant.tech/documentation/concepts/collections/#create-a-collection
    SPARSE_VECTOR_NAME: str = "langchain-sparse"

    def __init__(
        self,
        client: QdrantClient,
        collection_name: str,
        embedding: Embeddings | None = None,
        retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
        vector_name: str = VECTOR_NAME,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        distance: models.Distance = models.Distance.COSINE,
        sparse_embedding: SparseEmbeddings | None = None,
        sparse_vector_name: str = SPARSE_VECTOR_NAME,
        validate_embeddings: bool = True,  # noqa: FBT001, FBT002
        validate_collection_config: bool = True,  # noqa: FBT001, FBT002
    ) -> None:
        """Initialize a new instance of `QdrantVectorStore`.

        ```python
        qdrant = Qdrant(
            client=client,
            collection_name="my-collection",
            embedding=OpenAIEmbeddings(),
            retrieval_mode=RetrievalMode.HYBRID,
            sparse_embedding=FastEmbedSparse(),
        )
        ```
        """
        if validate_embeddings:
            self._validate_embeddings(retrieval_mode, embedding, sparse_embedding)

        if validate_collection_config:
            self._validate_collection_config(
                client,
                collection_name,
                retrieval_mode,
                vector_name,
                sparse_vector_name,
                distance,
                embedding,
            )

        self._client = client
        self.collection_name = collection_name
        self._embeddings = embedding
        self.retrieval_mode = retrieval_mode
        self.vector_name = vector_name
        self.content_payload_key = content_payload_key
        self.metadata_payload_key = metadata_payload_key
        self.distance = distance
        self._sparse_embeddings = sparse_embedding
        self.sparse_vector_name = sparse_vector_name

    @property
    def client(self) -> QdrantClient:
        """Get the Qdrant client instance that is being used.

        Returns:
            QdrantClient: An instance of `QdrantClient`.

        """
        return self._client

    @property
    def embeddings(self) -> Embeddings | None:
        """Get the dense embeddings instance that is being used.

        Returns:
            Embeddings: An instance of `Embeddings`, or None for SPARSE mode.

        """
        return self._embeddings

    def _get_retriever_tags(self) -> list[str]:
        """Get tags for retriever.

        Override the base class method to handle SPARSE mode where embeddings can be
        None. In SPARSE mode, embeddings is None, so we don't include embeddings class
        name in tags. In DENSE/HYBRID modes, embeddings is not None, so we include
        embeddings class name.
        """
        tags = [self.__class__.__name__]

        # Handle different retrieval modes
        if self.retrieval_mode == RetrievalMode.SPARSE:
            # SPARSE mode: no dense embeddings, so no embeddings class name in tags
            pass
        # DENSE/HYBRID modes: include embeddings class name if available
        elif self.embeddings is not None:
            tags.append(self.embeddings.__class__.__name__)

        return tags

    def _require_embeddings(self, operation: str) -> Embeddings:
        """Require embeddings for operations that need them.

        Args:
            operation: Description of the operation requiring embeddings.

        Returns:
            The embeddings instance.

        Raises:
            ValueError: If embeddings are None and required for the operation.
        """
        if self.embeddings is None:
            msg = f"Embeddings are required for {operation}"
            raise ValueError(msg)
        return self.embeddings

    @property
    def sparse_embeddings(self) -> SparseEmbeddings:
        """Get the sparse embeddings instance that is being used.

        Raises:
            ValueError: If sparse embeddings are `None`.

        Returns:
            SparseEmbeddings: An instance of `SparseEmbeddings`.

        """
        if self._sparse_embeddings is None:
            msg = (
                "Sparse embeddings are `None`. "
                "Please set using the `sparse_embedding` parameter."
            )
            raise ValueError(msg)
        return self._sparse_embeddings

    @classmethod
    def from_texts(
        cls: type[QdrantVectorStore],
        texts: list[str],
        embedding: Embeddings | None = None,
        metadatas: list[dict] | None = None,
        ids: Sequence[str | int] | None = None,
        collection_name: str | None = None,
        location: str | None = None,
        url: str | None = None,
        port: int | None = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,  # noqa: FBT001, FBT002
        https: bool | None = None,  # noqa: FBT001
        api_key: str | None = None,
        prefix: str | None = None,
        timeout: int | None = None,
        host: str | None = None,
        path: str | None = None,
        distance: models.Distance = models.Distance.COSINE,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: str = VECTOR_NAME,
        retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
        sparse_embedding: SparseEmbeddings | None = None,
        sparse_vector_name: str = SPARSE_VECTOR_NAME,
        collection_create_options: dict[str, Any] | None = None,
        vector_params: dict[str, Any] | None = None,
        sparse_vector_params: dict[str, Any] | None = None,
        batch_size: int = 64,
        force_recreate: bool = False,  # noqa: FBT001, FBT002
        validate_embeddings: bool = True,  # noqa: FBT001, FBT002
        validate_collection_config: bool = True,  # noqa: FBT001, FBT002
        **kwargs: Any,
    ) -> QdrantVectorStore:
        """Construct an instance of `QdrantVectorStore` from a list of texts.

        This is a user-friendly interface that:
        1. Creates embeddings, one for each text
        2. Creates a Qdrant collection if it doesn't exist.
        3. Adds the text embeddings to the Qdrant database

        This is intended to be a quick way to get started.

        ```python
        from langchain_qdrant import Qdrant
        from langchain_openai import OpenAIEmbeddings

        embeddings = OpenAIEmbeddings()
        qdrant = Qdrant.from_texts(texts, embeddings, url="http://localhost:6333")
        ```
        """
        if sparse_vector_params is None:
            sparse_vector_params = {}
        if vector_params is None:
            vector_params = {}
        if collection_create_options is None:
            collection_create_options = {}
        client_options = {
            "location": location,
            "url": url,
            "port": port,
            "grpc_port": grpc_port,
            "prefer_grpc": prefer_grpc,
            "https": https,
            "api_key": api_key,
            "prefix": prefix,
            "timeout": timeout,
            "host": host,
            "path": path,
            **kwargs,
        }

        qdrant = cls.construct_instance(
            embedding,
            retrieval_mode,
            sparse_embedding,
            client_options,
            collection_name,
            distance,
            content_payload_key,
            metadata_payload_key,
            vector_name,
            sparse_vector_name,
            force_recreate,
            collection_create_options,
            vector_params,
            sparse_vector_params,
            validate_embeddings,
            validate_collection_config,
        )
        qdrant.add_texts(texts, metadatas, ids, batch_size)
        return qdrant

    @classmethod
    def from_existing_collection(
        cls: type[QdrantVectorStore],
        collection_name: str,
        embedding: Embeddings | None = None,
        retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
        location: str | None = None,
        url: str | None = None,
        port: int | None = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,  # noqa: FBT001, FBT002
        https: bool | None = None,  # noqa: FBT001
        api_key: str | None = None,
        prefix: str | None = None,
        timeout: int | None = None,
        host: str | None = None,
        path: str | None = None,
        distance: models.Distance = models.Distance.COSINE,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: str = VECTOR_NAME,
        sparse_vector_name: str = SPARSE_VECTOR_NAME,
        sparse_embedding: SparseEmbeddings | None = None,
        validate_embeddings: bool = True,  # noqa: FBT001, FBT002
        validate_collection_config: bool = True,  # noqa: FBT001, FBT002
        **kwargs: Any,
    ) -> QdrantVectorStore:
        """Construct `QdrantVectorStore` from existing collection without adding data.

        Returns:
            QdrantVectorStore: A new instance of `QdrantVectorStore`.
        """
        client = QdrantClient(
            location=location,
            url=url,
            port=port,
            grpc_port=grpc_port,
            prefer_grpc=prefer_grpc,
            https=https,
            api_key=api_key,
            prefix=prefix,
            timeout=timeout,
            host=host,
            path=path,
            **kwargs,
        )

        return cls(
            client=client,
            collection_name=collection_name,
            embedding=embedding,
            retrieval_mode=retrieval_mode,
            content_payload_key=content_payload_key,
            metadata_payload_key=metadata_payload_key,
            distance=distance,
            vector_name=vector_name,
            sparse_embedding=sparse_embedding,
            sparse_vector_name=sparse_vector_name,
            validate_embeddings=validate_embeddings,
            validate_collection_config=validate_collection_config,
        )

    def add_texts(  # type: ignore[override]
        self,
        texts: Iterable[str],
        metadatas: list[dict] | None = None,
        ids: Sequence[str | int] | None = None,
        batch_size: int = 64,
        **kwargs: Any,
    ) -> list[str | int]:
        """Add texts with embeddings to the vectorstore.

        Returns:
            List of ids from adding the texts into the vectorstore.

        """
        added_ids = []
        for batch_ids, points in self._generate_batches(
            texts, metadatas, ids, batch_size
        ):
            self.client.upsert(
                collection_name=self.collection_name, points=points, **kwargs
            )
            added_ids.extend(batch_ids)

        return added_ids

    def similarity_search(
        self,
        query: str,
        k: int = 4,
        filter: models.Filter | None = None,  # noqa: A002
        search_params: models.SearchParams | None = None,
        offset: int = 0,
        score_threshold: float | None = None,
        consistency: models.ReadConsistency | None = None,
        hybrid_fusion: models.FusionQuery | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        """Return docs most similar to query.

        Returns:
            List of Documents most similar to the query.

        """
        results = self.similarity_search_with_score(
            query,
            k,
            filter=filter,
            search_params=search_params,
            offset=offset,
            score_threshold=score_threshold,
            consistency=consistency,
            hybrid_fusion=hybrid_fusion,
            **kwargs,
        )
        return list(map(itemgetter(0), results))

    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        filter: models.Filter | None = None,  # noqa: A002
        search_params: models.SearchParams | None = None,
        offset: int = 0,
        score_threshold: float | None = None,
        consistency: models.ReadConsistency | None = None,
        hybrid_fusion: models.FusionQuery | None = None,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Return docs most similar to query.

        Returns:
            List of documents most similar to the query text and distance for each.

        """
        query_options = {
            "collection_name": self.collection_name,
            "query_filter": filter,
            "search_params": search_params,
            "limit": k,
            "offset": offset,
            "with_payload": True,
            "with_vectors": False,
            "score_threshold": score_threshold,
            "consistency": consistency,
            **kwargs,
        }
        if self.retrieval_mode == RetrievalMode.DENSE:
            embeddings = self._require_embeddings("DENSE mode")
            query_dense_embedding = embeddings.embed_query(query)
            results = self.client.query_points(
                query=query_dense_embedding,
                using=self.vector_name,
                **query_options,
            ).points

        elif self.retrieval_mode == RetrievalMode.SPARSE:
            query_sparse_embedding = self.sparse_embeddings.embed_query(query)
            results = self.client.query_points(
                query=models.SparseVector(
                    indices=query_sparse_embedding.indices,
                    values=query_sparse_embedding.values,
                ),
                using=self.sparse_vector_name,
                **query_options,
            ).points

        elif self.retrieval_mode == RetrievalMode.HYBRID:
            embeddings = self._require_embeddings("HYBRID mode")
            query_dense_embedding = embeddings.embed_query(query)
            query_sparse_embedding = self.sparse_embeddings.embed_query(query)
            results = self.client.query_points(
                prefetch=[
                    models.Prefetch(
                        using=self.vector_name,
                        query=query_dense_embedding,
                        filter=filter,
                        limit=k,
                        params=search_params,
                    ),
                    models.Prefetch(
                        using=self.sparse_vector_name,
                        query=models.SparseVector(
                            indices=query_sparse_embedding.indices,
                            values=query_sparse_embedding.values,
                        ),
                        filter=filter,
                        limit=k,
                        params=search_params,
                    ),
                ],
                query=hybrid_fusion or models.FusionQuery(fusion=models.Fusion.RRF),
                **query_options,
            ).points

        else:
            msg = f"Invalid retrieval mode. {self.retrieval_mode}."
            raise ValueError(msg)
        return [
            (
                self._document_from_point(
                    result,
                    self.collection_name,
                    self.content_payload_key,
                    self.metadata_payload_key,
                ),
                result.score,
            )
            for result in results
        ]

    def similarity_search_with_score_by_vector(
        self,
        embedding: list[float],
        k: int = 4,
        filter: models.Filter | None = None,  # noqa: A002
        search_params: models.SearchParams | None = None,
        offset: int = 0,
        score_threshold: float | None = None,
        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Return docs most similar to embedding vector.

        Returns:
            List of Documents most similar to the query and distance for each.

        """
        qdrant_filter = filter

        self._validate_collection_for_dense(
            client=self.client,
            collection_name=self.collection_name,
            vector_name=self.vector_name,
            distance=self.distance,
            dense_embeddings=embedding,
        )
        results = self.client.query_points(
            collection_name=self.collection_name,
            query=embedding,
            using=self.vector_name,
            query_filter=qdrant_filter,
            search_params=search_params,
            limit=k,
            offset=offset,
            with_payload=True,
            with_vectors=False,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        ).points

        return [
            (
                self._document_from_point(
                    result,
                    self.collection_name,
                    self.content_payload_key,
                    self.metadata_payload_key,
                ),
                result.score,
            )
            for result in results
        ]

    def similarity_search_by_vector(
        self,
        embedding: list[float],
        k: int = 4,
        filter: models.Filter | None = None,  # noqa: A002
        search_params: models.SearchParams | None = None,
        offset: int = 0,
        score_threshold: float | None = None,
        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        """Return docs most similar to embedding vector.

        Returns:
            List of Documents most similar to the query.

        """
        results = self.similarity_search_with_score_by_vector(
            embedding,
            k,
            filter=filter,
            search_params=search_params,
            offset=offset,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
        return list(map(itemgetter(0), results))

    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: models.Filter | None = None,  # noqa: A002
        search_params: models.SearchParams | None = None,
        score_threshold: float | None = None,
        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        """Return docs selected using the maximal marginal relevance with dense vectors.

        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Returns:
            List of Documents selected by maximal marginal relevance.

        """
        self._validate_collection_for_dense(
            self.client,
            self.collection_name,
            self.vector_name,
            self.distance,
            self.embeddings,
        )

        embeddings = self._require_embeddings("max_marginal_relevance_search")
        query_embedding = embeddings.embed_query(query)
        return self.max_marginal_relevance_search_by_vector(
            query_embedding,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
            search_params=search_params,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )

    def max_marginal_relevance_search_by_vector(
        self,
        embedding: list[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: models.Filter | None = None,  # noqa: A002
        search_params: models.SearchParams | None = None,
        score_threshold: float | None = None,
        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        """Return docs selected using the maximal marginal relevance with dense vectors.

        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Returns:
            List of Documents selected by maximal marginal relevance.

        """
        results = self.max_marginal_relevance_search_with_score_by_vector(
            embedding,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
            search_params=search_params,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
        return list(map(itemgetter(0), results))

    def max_marginal_relevance_search_with_score_by_vector(
        self,
        embedding: list[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: models.Filter | None = None,  # noqa: A002
        search_params: models.SearchParams | None = None,
        score_threshold: float | None = None,
        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Return docs selected using the maximal marginal relevance.

        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Returns:
            List of Documents selected by maximal marginal relevance and distance for
            each.

        """
        results = self.client.query_points(
            collection_name=self.collection_name,
            query=embedding,
            query_filter=filter,
            search_params=search_params,
            limit=fetch_k,
            with_payload=True,
            with_vectors=True,
            score_threshold=score_threshold,
            consistency=consistency,
            using=self.vector_name,
            **kwargs,
        ).points

        embeddings = [
            result.vector
            if isinstance(result.vector, list)
            else result.vector.get(self.vector_name)  # type: ignore[union-attr]
            for result in results
        ]
        mmr_selected = maximal_marginal_relevance(
            np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
        )
        return [
            (
                self._document_from_point(
                    results[i],
                    self.collection_name,
                    self.content_payload_key,
                    self.metadata_payload_key,
                ),
                results[i].score,
            )
            for i in mmr_selected
        ]

    def delete(  # type: ignore[override]
        self,
        ids: list[str | int] | None = None,
        **kwargs: Any,
    ) -> bool | None:
        """Delete documents by their ids.

        Args:
            ids: List of ids to delete.
            **kwargs: Other keyword arguments that subclasses might use.

        Returns:
            True if deletion is successful, False otherwise.

        """
        result = self.client.delete(
            collection_name=self.collection_name,
            points_selector=ids,
        )
        return result.status == models.UpdateStatus.COMPLETED

    def get_by_ids(self, ids: Sequence[str | int], /) -> list[Document]:
        results = self.client.retrieve(self.collection_name, ids, with_payload=True)

        return [
            self._document_from_point(
                result,
                self.collection_name,
                self.content_payload_key,
                self.metadata_payload_key,
            )
            for result in results
        ]

    @classmethod
    def construct_instance(
        cls: type[QdrantVectorStore],
        embedding: Embeddings | None = None,
        retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
        sparse_embedding: SparseEmbeddings | None = None,
        client_options: dict[str, Any] | None = None,
        collection_name: str | None = None,
        distance: models.Distance = models.Distance.COSINE,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: str = VECTOR_NAME,
        sparse_vector_name: str = SPARSE_VECTOR_NAME,
        force_recreate: bool = False,  # noqa: FBT001, FBT002
        collection_create_options: dict[str, Any] | None = None,
        vector_params: dict[str, Any] | None = None,
        sparse_vector_params: dict[str, Any] | None = None,
        validate_embeddings: bool = True,  # noqa: FBT001, FBT002
        validate_collection_config: bool = True,  # noqa: FBT001, FBT002
    ) -> QdrantVectorStore:
        if sparse_vector_params is None:
            sparse_vector_params = {}
        if vector_params is None:
            vector_params = {}
        if collection_create_options is None:
            collection_create_options = {}
        if client_options is None:
            client_options = {}
        if validate_embeddings:
            cls._validate_embeddings(retrieval_mode, embedding, sparse_embedding)
        collection_name = collection_name or uuid.uuid4().hex
        client = QdrantClient(**client_options)

        collection_exists = client.collection_exists(collection_name)

        if collection_exists and force_recreate:
            client.delete_collection(collection_name)
            collection_exists = False
        if collection_exists:
            if validate_collection_config:
                cls._validate_collection_config(
                    client,
                    collection_name,
                    retrieval_mode,
                    vector_name,
                    sparse_vector_name,
                    distance,
                    embedding,
                )
        else:
            vectors_config, sparse_vectors_config = {}, {}
            if retrieval_mode == RetrievalMode.DENSE:
                partial_embeddings = embedding.embed_documents(["dummy_text"])  # type: ignore[union-attr]

                vector_params["size"] = len(partial_embeddings[0])
                vector_params["distance"] = distance

                vectors_config = {
                    vector_name: models.VectorParams(
                        **vector_params,
                    )
                }

            elif retrieval_mode == RetrievalMode.SPARSE:
                sparse_vectors_config = {
                    sparse_vector_name: models.SparseVectorParams(
                        **sparse_vector_params
                    )
                }

            elif retrieval_mode == RetrievalMode.HYBRID:
                partial_embeddings = embedding.embed_documents(["dummy_text"])  # type: ignore[union-attr]

                vector_params["size"] = len(partial_embeddings[0])
                vector_params["distance"] = distance

                vectors_config = {
                    vector_name: models.VectorParams(
                        **vector_params,
                    )
                }

                sparse_vectors_config = {
                    sparse_vector_name: models.SparseVectorParams(
                        **sparse_vector_params
                    )
                }

            collection_create_options["collection_name"] = collection_name
            collection_create_options["vectors_config"] = vectors_config
            collection_create_options["sparse_vectors_config"] = sparse_vectors_config

            client.create_collection(**collection_create_options)

        return cls(
            client=client,
            collection_name=collection_name,
            embedding=embedding,
            retrieval_mode=retrieval_mode,
            content_payload_key=content_payload_key,
            metadata_payload_key=metadata_payload_key,
            distance=distance,
            vector_name=vector_name,
            sparse_embedding=sparse_embedding,
            sparse_vector_name=sparse_vector_name,
            validate_embeddings=False,
            validate_collection_config=False,
        )

    @staticmethod
    def _cosine_relevance_score_fn(distance: float) -> float:
        """Normalize the distance to a score on a scale `[0, 1]`."""
        return (distance + 1.0) / 2.0

    def _select_relevance_score_fn(self) -> Callable[[float], float]:
        """Your "correct" relevance function may differ depending on a few things.

        Including:
        - The distance / similarity metric used by the VectorStore
        - The scale of your embeddings (OpenAI's are unit normed. Many others are not!)
        - Embedding dimensionality
        - etc.
        """
        if self.distance == models.Distance.COSINE:
            return self._cosine_relevance_score_fn
        if self.distance == models.Distance.DOT:
            return self._max_inner_product_relevance_score_fn
        if self.distance == models.Distance.EUCLID:
            return self._euclidean_relevance_score_fn
        msg = "Unknown distance strategy, must be COSINE, DOT, or EUCLID."
        raise ValueError(msg)

    @classmethod
    def _document_from_point(
        cls,
        scored_point: Any,
        collection_name: str,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> Document:
        metadata = scored_point.payload.get(metadata_payload_key) or {}
        metadata["_id"] = scored_point.id
        metadata["_collection_name"] = collection_name
        return Document(
            page_content=scored_point.payload.get(content_payload_key, ""),
            metadata=metadata,
        )

    def _generate_batches(
        self,
        texts: Iterable[str],
        metadatas: list[dict] | None = None,
        ids: Sequence[str | int] | None = None,
        batch_size: int = 64,
    ) -> Generator[tuple[list[str | int], list[models.PointStruct]], Any, None]:
        texts_iterator = iter(texts)
        metadatas_iterator = iter(metadatas or [])
        ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])

        while batch_texts := list(islice(texts_iterator, batch_size)):
            batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
            batch_ids = list(islice(ids_iterator, batch_size))
            points = [
                models.PointStruct(
                    id=point_id,
                    vector=vector,
                    payload=payload,
                )
                for point_id, vector, payload in zip(
                    batch_ids,
                    self._build_vectors(batch_texts),
                    self._build_payloads(
                        batch_texts,
                        batch_metadatas,
                        self.content_payload_key,
                        self.metadata_payload_key,
                    ),
                    strict=False,
                )
            ]

            yield batch_ids, points

    @staticmethod
    def _build_payloads(
        texts: Iterable[str],
        metadatas: list[dict] | None,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> list[dict]:
        payloads = []
        for i, text in enumerate(texts):
            if text is None:
                msg = (
                    "At least one of the texts is None. Please remove it before "
                    "calling .from_texts or .add_texts."
                )
                raise ValueError(msg)
            metadata = metadatas[i] if metadatas is not None else None
            payloads.append(
                {
                    content_payload_key: text,
                    metadata_payload_key: metadata,
                }
            )

        return payloads

    def _build_vectors(
        self,
        texts: Iterable[str],
    ) -> list[models.VectorStruct]:
        if self.retrieval_mode == RetrievalMode.DENSE:
            embeddings = self._require_embeddings("DENSE mode")
            batch_embeddings = embeddings.embed_documents(list(texts))
            return [
                {
                    self.vector_name: vector,
                }
                for vector in batch_embeddings
            ]

        if self.retrieval_mode == RetrievalMode.SPARSE:
            batch_sparse_embeddings = self.sparse_embeddings.embed_documents(
                list(texts)
            )
            return [
                {
                    self.sparse_vector_name: models.SparseVector(
                        values=vector.values, indices=vector.indices
                    )
                }
                for vector in batch_sparse_embeddings
            ]

        if self.retrieval_mode == RetrievalMode.HYBRID:
            embeddings = self._require_embeddings("HYBRID mode")
            dense_embeddings = embeddings.embed_documents(list(texts))
            sparse_embeddings = self.sparse_embeddings.embed_documents(list(texts))

            if len(dense_embeddings) != len(sparse_embeddings):
                msg = "Mismatched length between dense and sparse embeddings."
                raise ValueError(msg)

            return [
                {
                    self.vector_name: dense_vector,
                    self.sparse_vector_name: models.SparseVector(
                        values=sparse_vector.values, indices=sparse_vector.indices
                    ),
                }
                for dense_vector, sparse_vector in zip(
                    dense_embeddings, sparse_embeddings, strict=False
                )
            ]

        msg = f"Unknown retrieval mode. {self.retrieval_mode} to build vectors."
        raise ValueError(msg)

    @classmethod
    def _validate_collection_config(
        cls: type[QdrantVectorStore],
        client: QdrantClient,
        collection_name: str,
        retrieval_mode: RetrievalMode,
        vector_name: str,
        sparse_vector_name: str,
        distance: models.Distance,
        embedding: Embeddings | None,
    ) -> None:
        if retrieval_mode == RetrievalMode.DENSE:
            cls._validate_collection_for_dense(
                client, collection_name, vector_name, distance, embedding
            )

        elif retrieval_mode == RetrievalMode.SPARSE:
            cls._validate_collection_for_sparse(
                client, collection_name, sparse_vector_name
            )

        elif retrieval_mode == RetrievalMode.HYBRID:
            cls._validate_collection_for_dense(
                client, collection_name, vector_name, distance, embedding
            )
            cls._validate_collection_for_sparse(
                client, collection_name, sparse_vector_name
            )

    @classmethod
    def _validate_collection_for_dense(
        cls: type[QdrantVectorStore],
        client: QdrantClient,
        collection_name: str,
        vector_name: str,
        distance: models.Distance,
        dense_embeddings: Embeddings | list[float] | None,
    ) -> None:
        collection_info = client.get_collection(collection_name=collection_name)
        vector_config = collection_info.config.params.vectors

        if isinstance(vector_config, dict):
            # vector_config is a Dict[str, VectorParams]
            if vector_name not in vector_config:
                msg = (
                    f"Existing Qdrant collection {collection_name} does not "
                    f"contain dense vector named {vector_name}. "
                    "Did you mean one of the "
                    f"existing vectors: {', '.join(vector_config.keys())}? "  # type: ignore[union-attr]
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
                raise QdrantVectorStoreError(msg)

            # Get the VectorParams object for the specified vector_name
            vector_config = vector_config[vector_name]  # type: ignore[assignment, index]

        # vector_config is an instance of VectorParams
        # Case of a collection with single/unnamed vector.
        elif vector_name != "":
            msg = (
                f"Existing Qdrant collection {collection_name} is built "
                "with unnamed dense vector. "
                f"If you want to reuse it, set `vector_name` to ''(empty string)."
                f"If you want to recreate the collection, "
                "set `force_recreate` to `True`."
            )
            raise QdrantVectorStoreError(msg)

        if vector_config is None:
            msg = "VectorParams is None"
            raise ValueError(msg)

        if isinstance(dense_embeddings, Embeddings):
            vector_size = len(dense_embeddings.embed_documents(["dummy_text"])[0])
        elif isinstance(dense_embeddings, list):
            vector_size = len(dense_embeddings)
        else:
            msg = "Invalid `embeddings` type."
            raise TypeError(msg)

        if vector_config.size != vector_size:
            msg = (
                f"Existing Qdrant collection is configured for dense vectors with "
                f"{vector_config.size} dimensions. "
                f"Selected embeddings are {vector_size}-dimensional. "
                f"If you want to recreate the collection, set `force_recreate` "
                f"parameter to `True`."
            )
            raise QdrantVectorStoreError(msg)

        if vector_config.distance != distance:
            msg = (
                f"Existing Qdrant collection is configured for "
                f"{vector_config.distance.name} similarity, but requested "
                f"{distance.upper()}. Please set `distance` parameter to "
                f"`{vector_config.distance.name}` if you want to reuse it. "
                f"If you want to recreate the collection, set `force_recreate` "
                f"parameter to `True`."
            )
            raise QdrantVectorStoreError(msg)

    @classmethod
    def _validate_collection_for_sparse(
        cls: type[QdrantVectorStore],
        client: QdrantClient,
        collection_name: str,
        sparse_vector_name: str,
    ) -> None:
        collection_info = client.get_collection(collection_name=collection_name)
        sparse_vector_config = collection_info.config.params.sparse_vectors

        if (
            sparse_vector_config is None
            or sparse_vector_name not in sparse_vector_config
        ):
            msg = (
                f"Existing Qdrant collection {collection_name} does not "
                f"contain sparse vectors named {sparse_vector_name}. "
                f"If you want to recreate the collection, set `force_recreate` "
                f"parameter to `True`."
            )
            raise QdrantVectorStoreError(msg)

    @classmethod
    def _validate_embeddings(
        cls: type[QdrantVectorStore],
        retrieval_mode: RetrievalMode,
        embedding: Embeddings | None,
        sparse_embedding: SparseEmbeddings | None,
    ) -> None:
        if retrieval_mode == RetrievalMode.DENSE and embedding is None:
            msg = "'embedding' cannot be None when retrieval mode is 'dense'"
            raise ValueError(msg)

        if retrieval_mode == RetrievalMode.SPARSE and sparse_embedding is None:
            msg = "'sparse_embedding' cannot be None when retrieval mode is 'sparse'"
            raise ValueError(msg)

        if retrieval_mode == RetrievalMode.HYBRID and any(
            [embedding is None, sparse_embedding is None]
        ):
            msg = (
                "Both 'embedding' and 'sparse_embedding' cannot be None "
                "when retrieval mode is 'hybrid'"
            )
            raise ValueError(msg)
